The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present. The motivations for forgetting include privacy protection, copyright protection, elimination of biases and discrimination, and prevention of harmful content generation. Effective digital forgetting has to be effective (meaning how well the new model has forgotten the undesired knowledge/behavior), retain the performance of the original model on the desirable tasks, and be scalable (in particular forgetting has to be more efficient than retraining from scratch on just the tasks/data to be retained). This survey focuses on forgetting in large language models (LLMs). We first provide background on LLMs, including their components, the types of LLMs, and their usual training pipeline. Second, we describe the motivations, types, and desired properties of digital forgetting. Third, we introduce the approaches to digital forgetting in LLMs, among which unlearning methodologies stand out as the state of the art. Fourth, we provide a detailed taxonomy of machine unlearning methods for LLMs, and we survey and compare current approaches. Fifth, we detail datasets, models and metrics used for the evaluation of forgetting, retaining and runtime. Sixth, we discuss challenges in the area. Finally, we provide some concluding remarks.

翻译：数字遗忘的目标是，给定一个包含有害知识或行为的模型，获得一个其中已检测问题不再存在的新模型。遗忘的动机包括隐私保护、版权保护、消除偏见与歧视，以及防止有害内容生成。有效的数字遗忘必须具有高效性（即新模型遗忘有害知识或行为的程度）、保留原始模型在理想任务上的性能，以及可扩展性（尤其遗忘必须比仅基于需保留任务/数据重新从头训练更高效）。本综述聚焦于大语言模型中的遗忘。首先，我们提供大语言模型的背景知识，包括其组件、类型及常规训练流程。其次，描述数字遗忘的动机、类型及期望特性。第三，介绍大语言模型中数字遗忘的方法，其中去学习方法是当前最前沿的技术。第四，系统提出大语言模型机器去学习方法的详细分类法，并对现有方法进行综述与比较。第五，详细阐述用于遗忘效果、保留能力和运行时评估的数据集、模型及指标。第六，讨论该领域面临的挑战。最后，给出总结性评述。